OECD Data Live dataset

Data - OECD

Author

Info

source	dataset	.html	.RData
oecd	DP_LIVE	2024-12-21	2024-12-22

Data on macro

source	dataset	.html	.RData
eurostat	nama_10_a10	2024-11-23	2024-10-08
eurostat	nama_10_a10_e	2024-11-23	2024-12-22
eurostat	nama_10_gdp	2024-12-21	2024-12-21
eurostat	nama_10_lp_ulc	2024-11-23	2024-10-08
eurostat	namq_10_a10	2024-12-22	2024-12-22
eurostat	namq_10_a10_e	2024-12-22	2024-12-22
eurostat	namq_10_gdp	2024-12-21	2024-12-21
eurostat	namq_10_lp_ulc	2024-11-23	2024-11-04
eurostat	namq_10_pc	2024-11-23	2024-11-21
eurostat	nasa_10_nf_tr	2024-12-14	2024-12-14
eurostat	nasq_10_nf_tr	2024-11-23	2024-10-09
fred	gdp	2024-12-22	2024-12-22
oecd	QNA	2024-06-06	2024-12-22
oecd	SNA_TABLE1	2024-12-22	2024-12-22
oecd	SNA_TABLE14A	2024-09-15	2024-06-30
oecd	SNA_TABLE2	2024-07-01	2024-04-11
oecd	SNA_TABLE6A	2024-07-01	2024-06-30
wdi	NE.RSB.GNFS.ZS	2024-09-18	2024-09-18
wdi	NY.GDP.MKTP.CD	2024-09-18	2024-09-26
wdi	NY.GDP.MKTP.PP.CD	2024-09-18	2024-09-18
wdi	NY.GDP.PCAP.CD	2024-12-16	2024-12-22
wdi	NY.GDP.PCAP.KD	2024-09-18	2024-09-18
wdi	NY.GDP.PCAP.PP.CD	2024-12-22	2024-12-22
wdi	NY.GDP.PCAP.PP.KD	2024-12-22	2024-12-22

LAST_COMPILE

LAST_COMPILE
2024-12-22

Last

obsTime	Nobs
2019-Q4	3645

INDICATOR

Code

DP_LIVE %>%
  left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
  group_by(INDICATOR, Indicator) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SUBJECT

Code

DP_LIVE %>%
  left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
  group_by(SUBJECT, Subject) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

INDICATOR, SUBJECT

Code

DP_LIVE %>%
  left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
  left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
  group_by(INDICATOR, Indicator, SUBJECT, Subject) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

MEASURE

Code

DP_LIVE %>%
  left_join(DP_LIVE_var$MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

LOCATION

Code

DP_LIVE %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

FREQUENCY

Code

DP_LIVE %>%
  left_join(DP_LIVE_var$FREQUENCY, by = "FREQUENCY") %>%
  group_by(FREQUENCY, Frequency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

FREQUENCY	Frequency	Nobs
A	Annual	1230866
M	Monthly	694441
Q	Quarterly	476659

INDICATOR, SUBJECT, FREQUENCY

Code

DP_LIVE %>%
  left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
  left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
  group_by(INDICATOR, Indicator, SUBJECT, Subject, FREQUENCY) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

obsTime

Code

DP_LIVE %>%
  group_by(obsTime) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(obsTime)) %>%
  print_table_conditional()

Saving Rate - SAVING

See: https://data.oecd.org/natincome/saving-rate.htm

Saving is equal to the difference between disposable income (including an adjustment for the change in employment-related pension entitlements) and final consumption expenditure. It reflects the part of disposable income that, together with the incurrence of liabilities, is available to acquire financial and non-financial assets. The saving rate presented here corresponds to net saving, which is saving net of depreciation, as percentage of gross domestic product (GDP). All OECD countries compile their data according to the 2008 System of National Accounts (SNA).

United States, France

Code

DP_LIVE %>%
  filter(INDICATOR == "SAVING",
         LOCATION %in% c("FRA", "USA"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Saving Rate (% of GDP)") +
  add_2flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(-100, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

United States, France, Germany, Italy

Code

DP_LIVE %>%
  filter(INDICATOR == "SAVING",
         LOCATION %in% c("FRA", "USA", "DEU", "ITA"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Saving Rate (% of GDP)") +
  add_4flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(-100, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Money

Narrow Money - M1

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "M1",
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  select_if(~ n_distinct(.) > 1) %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n(),
            obsValue = last(obsValue)) %>%
  arrange(-obsValue) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Japan, United Kingdom, USA

Code

DP_LIVE %>%
  filter(LOCATION %in% c("JPN", "GBR", "USA"),
         INDICATOR == "M1",
         FREQUENCY == "M") %>%
  month_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Narrow Money - M1") +
  scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))

Euro area, U.S.

Code

DP_LIVE %>%
  filter(LOCATION %in% c("EA19", "USA", "JPN"),
         INDICATOR == "M1",
         FREQUENCY == "M") %>%
  month_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Narrow Money - M1") +
  scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))

Broad Money - M3

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "M3",
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  select_if(~ n_distinct(.) > 1) %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n(),
            obsValue = last(obsValue)) %>%
  arrange(-obsValue) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Japan, United Kingdom, USA

Code

DP_LIVE %>%
  filter(LOCATION %in% c("JPN", "GBR", "USA"),
         INDICATOR == "M3",
         FREQUENCY == "M") %>%
  month_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Broad Money - M3") +
  scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))

Euro area, U.S.

Code

DP_LIVE %>%
  filter(LOCATION %in% c("EA19", "USA", "JPN"),
         INDICATOR == "M3",
         FREQUENCY == "M") %>%
  month_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Broad Money - M3") +
  scale_x_date(breaks = seq(1940, 2030, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(1, 2, 3, 5, 10, 20, 50, 80, 100, 200, 300, 500, 800))

Gross domestic spending on R&D - GDEXPRD

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "GDEXPRD",
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime),
            obsValue = last(obsValue)) %>%
  arrange(-obsValue) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Korea, Israel, Sweden, Japan, Taiwan

Code

DP_LIVE %>%
  filter(INDICATOR == "GDEXPRD",
         LOCATION %in% c("KOR", "ISR", "SWE", "JPN", "TWN"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(Location = ifelse(LOCATION == "TWN", "Taiwan", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(Location = ifelse(LOCATION == "TWN", "Taiwan", Location)) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Gross domestic spending on R&D") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, .2),
                     labels = percent_format(accuracy = .1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

United States, France, Germany, Italy, Japan

Code

DP_LIVE %>%
  filter(INDICATOR == "GDEXPRD",
         LOCATION %in% c("USA", "FRA", "DEU", "ITA", "JPN"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Gross domestic spending on R&D") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, .2),
                     labels = percent_format(accuracy = .1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Spain, Portugal

Code

DP_LIVE %>%
  filter(INDICATOR == "GDEXPRD",
         LOCATION %in% c("ESP", "PRT"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Gross domestic spending on R&D") +
  add_2flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, .2),
                     labels = percent_format(accuracy = .1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Social Spending - SOCEXP, PENSIONEXP

Table

Code

DP_LIVE %>%
  filter(INDICATOR %in% c("SOCEXP", "PENSIONEXP"),
         MEASURE == "PC_GDP",
         obsTime == "2019") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  filter(!is.na(obsValue)) %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime),
            obsValue = last(obsValue)) %>%
  mutate(obsValue = obsValue/10^6/0.14) %>%
  arrange(-obsValue) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

OILPROD - Crude oil production

Info

Crude oil production in DP_LIVE is expressed as KTOE - thousands of ton of oil equivalent. (toe)

95 Millions of barrels / day = 34.7 Bn of barrels / year
1 barrel of oil equivalent (boe) = 0.14 ton of oil equivalent (toe).
1 ton of oil equivalent = 1/0.14 = 7.14 barrels of oil equivalent.

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "OILPROD") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  select_if(~ n_distinct(.) > 1) %>%
  filter(!is.na(obsValue)) %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime),
            obsValue = last(obsValue)) %>%
  mutate(obsValue = obsValue/10^6/0.14) %>%
  arrange(-obsValue) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table - World

Code

OILPRODWLD <- DP_LIVE %>%
  filter(INDICATOR == "OILPROD",
         LOCATION == "WLD") %>%
  select_if(~n_distinct(.) > 1) %>%
  filter(!is.na(obsValue)) %>%
  year_to_date %>%
  transmute(date, variable = "OILPRODWLD", value = obsValue)

save(OILPRODWLD, file = "DP_LIVE_OILPRODWLD.RData")

World, Saudi Arabia, Russia

Code

DP_LIVE %>%
  filter(INDICATOR == "OILPROD",
         LOCATION %in% c("WLD", "RUS", "SAU")) %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
  filter(!is.na(obsValue)) %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/10^6/0.14) %>%
  # 0.14 = 
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Billions of Barrels of Oil") +
  add_3flags + scale_color_identity() +
  scale_y_log10(breaks = c(1, 2, 3, 4, 5, 8, 10, 15, 20, 30),
                labels = dollar_format(a = 1, pre = "", su = " Bn")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

USA, Iraq, Iran

Code

DP_LIVE %>%
  filter(INDICATOR == "OILPROD",
         LOCATION %in% c("USA", "IRQ", "IRN")) %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  left_join(DP_LIVE_var$INDICATOR, by = "INDICATOR") %>%
  filter(!is.na(obsValue)) %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/10^6/0.14) %>%
  # 0.14 = 
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Billions of Barrels of Oil") +
  add_3flags + scale_color_identity() +
  scale_y_log10(breaks = c(0.1, 0.2 ,0.5, 0.8, 1, 2, 3, 4, 5, 8, 10, 15, 20, 30),
                labels = dollar_format(a = .1, pre = "", su = " Bn")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

GGEXP - General government spending

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime),
            obsValue = last(obsValue)) %>%
  arrange(-obsValue) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

United States, Europe

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("FRA", "USA"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_2flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Spain, Italy, France, Germany, Portugal

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("ESP", "ITA", "FRA", "DEU", "PRT", "USA"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending (% of GDP)") +
  add_6flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

France, Finland, Belgium, Norway, Denmark

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("FRA", "FIN", "BEL", "NOR", "DNK"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Sweden, Italy, Austria, Luxembourg, Greece

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("SWE", "ITA", "AUT", "LUX", "GRC"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Hungary, Germany, Australia, Iceland, Slovenia

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("HUN", "DEU", "AUS", "ISL", "SVN"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Slovak Republic, Portugal, Spain, Netherlands, Poland

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("SVK", "PRT", "ESP", "NLD", "POL"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Czech Republic, United Kingdom, Israel, Estonia, Japan

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("CZE", "GBR", "ISR", "EST", "JPN"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Latvia, United States, Lithuania, Switzerland, Costa Rica

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("LVA", "USA", "LTU", "CHE", "CRI"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_5flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

Colombia, Korea, Chile, Ireland

Code

DP_LIVE %>%
  filter(INDICATOR == "GGEXP",
         LOCATION %in% c("COL", "KOR", "CHL", "IRL"),
         SUBJECT == "TOT",
         MEASURE == "PC_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("General government spending") +
  add_4flags + scale_color_identity() +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 2),
                     labels = percent_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2100, 5), "-01-01")),
               labels = date_format("%Y"))

PASSCAR - Passenger car registrations

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "PASSCAR") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location, FREQUENCY) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

SHPRICE - Share Prices

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "SHPRICE") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

United States, Europe

Log

Code

DP_LIVE %>%
  filter(INDICATOR == "SHPRICE",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  year_to_date() %>%
  filter(date >= as.Date("2001-01-01")) %>%
  group_by(LOCATION) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Share Prices") +
  add_2flags + scale_color_identity() +
  scale_y_log10(breaks = seq(0, 260, 20),
                     labels = scales::dollar_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(2001, 2027, 2), "-01-01")),
               labels = date_format("%Y"))

Linear

Code

DP_LIVE %>%
  filter(INDICATOR == "SHPRICE",
         LOCATION %in% c("EA19", "USA"),
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  year_to_date() %>%
  filter(date >= as.Date("2001-01-01")) %>%
  group_by(LOCATION) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("Share Prices") +
  add_2flags + scale_color_identity() +
  scale_y_continuous(breaks = seq(0, 260, 20),
                     labels = scales::dollar_format(accuracy = 1, p = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(2001, 2027, 2), "-01-01")),
               labels = date_format("%Y"))

HHDEBT - Household Debt

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "HHDEBT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime)) %>%
  arrange(as.numeric(date_min), -as.numeric(date_max)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

France, Germany, Japan

Code

DP_LIVE %>%
  filter(LOCATION %in% c("FRA", "DEU", "JPN"),
         INDICATOR == "HHDEBT") %>%
  year_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue / 100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Household Debt (% of net disposable income)") +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 200, 5),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Canada, Denmark, Finland

Code

DP_LIVE %>%
  filter(LOCATION %in% c("CAN", "DNK", "FIN"),
         INDICATOR == "HHDEBT") %>%
  year_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue / 100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Household Debt (% of net disposable income)") +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

Japan, United Kingdom, United States

Code

DP_LIVE %>%
  filter(LOCATION %in% c("JPN", "GBR", "USA"),
         INDICATOR == "HHDEBT") %>%
  year_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue / 100) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Household Debt (% of net disposable income)") +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
                     labels = percent_format(accuracy = 1, prefix = "")) +
  theme(legend.position = c(0.2, 0.90),
        legend.title = element_blank())

GDPHRWKD - GDP Per hour worked

USD (constant prices 2010 and PPPs)

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "GDPHRWKD") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location, MEASURE) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

5 countries, IDX2010

Code

DP_LIVE %>%
  filter(INDICATOR == "GDPHRWKD",
         LOCATION %in% c("GBR", "USA", "FRA", "JPN", "ITA"),
         MEASURE == "IDX2010") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Average Wages") +
  geom_line(aes(x = date, y = obsValue, color = color, linetype = Location)) +
  #scale_linetype_manual(values = c("dotted", "solid", "longdash","solid",  "solid")) +
  add_5flags + scale_color_identity() +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 260, 5),
                     labels = scales::dollar_format(accuracy = 1, p = "", su = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
               labels = date_format("%Y"))

U.S. Dollars

UK, US, France, Japan, Italy

Code

DP_LIVE %>%
  filter(INDICATOR == "GDPHRWKD",
         LOCATION %in% c("GBR", "USA", "FRA", "JPN", "ITA"),
         MEASURE == "USD") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Average Wages") +
  geom_line(aes(x = date, y = obsValue, color = color, linetype = Location)) +
  #scale_linetype_manual(values = c("dotted", "solid", "longdash","solid",  "solid")) +
  add_5flags + scale_color_identity() +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 260, 5),
                     labels = scales::dollar_format(accuracy = 1, p = "$", su = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
               labels = date_format("%Y"))

Greece, UK

Code

DP_LIVE %>%
  filter(INDICATOR == "GDPHRWKD",
         LOCATION %in% c("GRC", "GBR"),
         MEASURE == "USD") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  year_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("GDP Per Hour Worked (Source: OECD)") +
  add_2flags +
  geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#0D5EAF", "#CF142B")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 260, 5),
                     labels = scales::dollar_format(accuracy = 1, p = "$")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
               labels = date_format("%Y"))

Greece, UK, Italy

Code

DP_LIVE %>%
  filter(INDICATOR == "GDPHRWKD",
         LOCATION %in% c("GRC", "GBR", "ITA"),
         MEASURE == "USD") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  year_to_date() %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("GDP Per Hour Worked (Source: OECD)") +
  add_3flags +
  geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#0D5EAF", "#009246", "#CF142B")) +
  scale_linetype_manual(values = c("solid", "solid", "longdash","solid",  "solid")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 260, 5),
                     labels = scales::dollar_format(accuracy = 1, p = "$")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
               labels = date_format("%Y"))

AVWAGE - General government debt

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "AVWAGE") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

5 countries

Code

DP_LIVE %>%
  filter(INDICATOR == "AVWAGE",
         LOCATION %in% c("GBR", "USA", "FRA", "JPN", "ITA")) %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Average Wages") +
  geom_line(aes(x = date, y = obsValue / 1000, color = Location, linetype = Location)) +
  scale_color_manual(values = c("#000000", "#009246", "#BC002D", "#6E82B5", "#B22234")) +
  scale_linetype_manual(values = c("solid", "solid", "longdash","solid",  "solid")) +
  geom_image(data = . %>%
               filter(date == as.Date("1990-01-01")) %>%
               mutate(date = as.Date("1990-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue/1000, image = image), asp = 1.5) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 260, 5),
                     labels = scales::dollar_format(accuracy = 1, p = "$", su = "k")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
               labels = date_format("%Y"))

GGDEBT - General government debt

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "GGDEBT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  arrange(obsTime) %>%
  summarise(date_min = min(obsTime),
            date_max = max(obsTime)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

5 countries

Code

DP_LIVE %>%
  filter(INDICATOR == "GGDEBT",
         LOCATION %in% c("GBR", "USA", "FRA", "JPN", "ITA")) %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Government Debt (% of GDP)") +
  geom_line(aes(x = date, y = obsValue / 100, color = Location, linetype = Location)) +
  scale_color_manual(values = c("#000000", "#009246", "#BC002D", "#6E82B5", "#B22234")) +
  scale_linetype_manual(values = c("solid", "solid", "longdash","solid",  "solid")) +
  geom_image(data = . %>%
               filter(date == as.Date("1997-01-01")) %>%
               mutate(date = as.Date("1997-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue/100, image = image), asp = 1.5) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
               labels = date_format("%Y"))

5 countries

Code

DP_LIVE %>%
  filter(INDICATOR == "GGDEBT",
         LOCATION %in% c("GBR", "USA", "DEU", "JPN", "ITA", "FRA")) %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Dette publique (années de PIB)") +
  geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + add_6flags +
  scale_linetype_manual(values = c("solid", "solid", "longdash","solid",  "solid")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 260, 20),
                     labels = scales::dollar_format(acc = .1, pre = "", su = " années")) +
  scale_x_date(breaks = as.Date(paste0(seq(1700, 2020, 5), "-01-01")),
               labels = date_format("%Y"))

Labour force participation rate

Table, 2019

Code

DP_LIVE %>%
  filter(obsTime == "2019",
         INDICATOR == "LFPR") %>%
  select(LOCATION, SUBJECT, obsValue) %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  mutate(obsValue = round(obsValue, 1)) %>%
  spread(SUBJECT, obsValue) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Netherlands, Germany, Japan

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "JPN", "NLD"), 
         INDICATOR == "LFPR",
         SUBJECT == "15_64") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  ggplot() + geom_line(aes(x = date, y = obsValue/100, color = Location)) +
  scale_color_manual(values = c("#000000", "#BC002D", "#21468B")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Labour force participation rate") + xlab("")

Employment Rates

Netherlands, Germany, Japan

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "JPN", "NLD"), 
         INDICATOR == "EMP", 
         SUBJECT == "TOT",
         MEASURE == "PC_WKGPOP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#000000", "#BC002D", "#21468B")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Employment rates") + xlab("")

France, Italy, Spain

Code

DP_LIVE %>%
  filter(LOCATION %in% c("FRA", "ITA", "ESP"), 
         INDICATOR == "EMP", 
         SUBJECT == "TOT",
         MEASURE == "PC_WKGPOP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#002395", "#009246", "#C60B1E")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Part-time employment rate") + xlab("")

Gross Pension Replacement Rates

Men

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "FRA", "SWE"), 
         INDICATOR == "GPENSION", 
         SUBJECT == "MEN") %>%
  left_join(DP_LIVE_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#ED2939", "#000000", "#006AA7")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.3),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Gross Pension Replacement Rate") + xlab("")

Women

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "FRA", "SWE"), 
         INDICATOR == "GPENSION", 
         SUBJECT == "WOMEN") %>%
  left_join(DP_LIVE_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#ED2939", "#000000", "#006AA7")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.3),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Gross Pension Replacement Rate") + xlab("")

INPROD

Just Manufacturing

Japan, United Kingdom, USA

Code

DP_LIVE %>%
  filter(LOCATION %in% c("JPN", "GBR", "USA"),
         INDICATOR == "INDPROD",
         SUBJECT == "MFG",
         FREQUENCY == "M") %>%
  month_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_3flags +
  theme_minimal() + xlab("") + ylab("Industrial Production") +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-10, 400, 10))

Spain, Italy, France, Germany

All

Code

DP_LIVE %>%
  filter(LOCATION %in% c("ESP", "ITA", "FRA", "DEU"),
         INDICATOR == "INDPROD",
         SUBJECT == "MFG",
         FREQUENCY == "M") %>%
  month_to_date %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_4flags +
  theme_minimal() + xlab("") + ylab("Industrial Production") +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-10, 400, 10))

1990-

Code

DP_LIVE %>%
  filter(LOCATION %in% c("ESP", "ITA", "FRA", "DEU"),
         INDICATOR == "INDPROD",
         SUBJECT == "MFG",
         FREQUENCY == "M") %>%
  month_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[1]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) + 
  scale_color_identity() + add_4flags +
  theme_minimal() + xlab("") + ylab("Industrial Production") +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-10, 400, 10))

Germany

All

Code

DP_LIVE %>%
  filter(INDICATOR == "INDPROD", 
         LOCATION == "DEU", 
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = Subject))+ 
  theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-10, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

1990-

Code

DP_LIVE %>%
  filter(INDICATOR == "INDPROD", 
         LOCATION == "DEU", 
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$SUBJECT, by = "SUBJECT") %>%
  month_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = Subject))+ 
  theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-10, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

France

All

Code

DP_LIVE %>%
  filter(INDICATOR == "INDPROD", 
         LOCATION == "FRA", 
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$SUBJECT %>%
              setNames(c("SUBJECT", "Subject")), by = "SUBJECT") %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = Subject))+ 
  theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-10, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

1990-

Code

DP_LIVE %>%
  filter(INDICATOR == "INDPROD", 
         LOCATION == "FRA", 
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$SUBJECT %>%
              setNames(c("SUBJECT", "Subject")), by = "SUBJECT") %>%
  month_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = Subject))+ 
  theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-10, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

U.S.

Code

DP_LIVE %>%
  filter(INDICATOR == "INDPROD", 
         LOCATION == "USA", 
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$SUBJECT %>%
              setNames(c("SUBJECT", "Subject")), by = "SUBJECT") %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = Subject))+ 
  theme_minimal() + xlab("") + ylab("Industrial Production, Index") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-10, 200, 10),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.8, 0.20),
        legend.title = element_blank())

LT interest rates

All

Code

DP_LIVE %>%
  filter(INDICATOR == "LTINT", 
         LOCATION %in% c("USA", "FRA", "DEU"), 
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = obsValue/100, color = Location)) + 
  geom_image(data = . %>%
               filter(date == as.Date("1982-01-01")) %>%
               mutate(date = as.Date("1982-01-01"),
                      image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue/100, image = image), asp = 1.5) +
  theme_minimal() + xlab("") + ylab("Long-term interest rates") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-2, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = c("#002395", "#000000", "#B22234")) +
  theme(legend.position = c(0.8, 0.80),
        legend.title = element_blank())

2000-

Code

DP_LIVE %>%
  filter(INDICATOR == "LTINT", 
         LOCATION %in% c("USA", "FRA", "DEU"), 
         FREQUENCY == "M") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  month_to_date %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = obsValue/100, color = Location))+ 
  geom_image(data = . %>%
               filter(date == as.Date("2018-07-01")) %>%
               mutate(date = as.Date("2018-07-01"),
                      image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue/100, image = image), asp = 1.5) +
  theme_minimal() + xlab("") + ylab("Long-term interest rates") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-2, 30, 1),
                     labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = c("#002395", "#000000", "#B22234")) +
  theme(legend.position = c(0.8, 0.80),
        legend.title = element_blank())

Part Time Employment Rate

Table

Code

DP_LIVE %>%
  filter(INDICATOR == "PARTEMP",
         SUBJECT == "TOT") %>%
  group_by(LOCATION) %>%
  summarise(Nobs = n())

# # A tibble: 52 × 2
#    LOCATION  Nobs
#    <chr>    <int>
#  1 AUS         24
#  2 AUT         30
#  3 BEL         42
#  4 BGR         25
#  5 BRA         23
#  6 CAN         49
#  7 CHE         29
#  8 CHL         29
#  9 COL         23
# 10 CRI         15
# # ℹ 42 more rows

Netherlands, Germany, Japan

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "JPN", "NLD"), 
         INDICATOR == "PARTEMP", 
         SUBJECT == "TOT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#000000", "#BC002D", "#21468B")) + add_3flags +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Part-time employment rate") + xlab("")

Netherlands, France, Germany, Japan

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "FRA", "JPN", "NLD"), 
         INDICATOR == "PARTEMP", 
         SUBJECT == "TOT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  add_4flags +
  scale_color_manual(values = c("#002395", "#000000", "#BC002D", "#21468B")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Part-time employment rate") + xlab("")

Germany, France

English

All

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "FRA"), 
         INDICATOR == "PARTEMP", 
         SUBJECT == "TOT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#002395", "#000000")) +
  geom_image(data = . %>%
               filter(date == as.Date("2006-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue, image = image), asp = 1.5) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Part-time employment rate") + xlab("")

1995-

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "FRA"), 
         INDICATOR == "PARTEMP", 
         SUBJECT == "TOT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#002395", "#000000")) +
  geom_image(data = . %>%
               filter(date == as.Date("2006-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue, image = image), asp = 1.5) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Taux d'emploi à temps partiel (%)") + xlab("")

French

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "FRA"), 
         INDICATOR == "PARTEMP", 
         SUBJECT == "TOT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#002395", "#000000")) +
  geom_image(data = . %>%
               filter(date == as.Date("2006-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue, image = image), asp = 1.5) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.24)) +
  ylab("Taux d'emploi à temps partiel") + xlab("")

French

Code

DP_LIVE %>%
  filter(LOCATION %in% c("DEU", "FRA"), 
         INDICATOR == "PARTEMP", 
         SUBJECT == "TOT") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  year_to_date() %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = Location)) +
  scale_color_manual(values = c("#002395", "#000000")) +
  geom_image(data = . %>%
               filter(date == as.Date("2006-01-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Location)), ".png")),
             aes(x = date, y = obsValue, image = image), asp = 1.5) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_continuous(breaks = 0.01*seq(-6, 90, 2),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.24)) +
  ylab("Taux d'emploi à temps partiel") + xlab("")

Tourism GDP

Code

DP_LIVE %>%
  filter(INDICATOR == "TOUR_GDP") %>%
  left_join(DP_LIVE_var$LOCATION, by = "LOCATION") %>%
  arrange(LOCATION, obsTime) %>%
  group_by(LOCATION, Location) %>%
  summarise(year = last(obsTime),
            `Tourism (% of GDP)` = round(last(obsValue), 1)) %>%
  arrange(-`Tourism (% of GDP)`) %>%
  mutate(`Tourism (% of GDP)` = paste0(`Tourism (% of GDP)`, "%")) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}